Biology subjects

Ng, W. Y.

Publications and source records attributed to Ng, W. Y..

3 recordsLinked to original sources

bioRxiv2026

Modeling pancreatic cancer tumor stroma co-evolution in an in ovo model

Pancreatic ductal adenocarcinoma (PDAC) is characterized by a dense, desmoplastic microenvironment that drives disease progression, yet conventional models fail to capture this complex tumor-stroma coevolution. Here, we utilize the chick chorioallantoic membrane (CAM) platform to investigate tumor-stroma interactions using murine PDAC cell lines and patient-derived organoids (PDOs). Integrating single-cell RNA sequencing and spatial transcriptomics, we show that the CAM microenvironment supports the emergence of complex tumor ecosystems while preserving patient-specific characteristics. Within five days, in ovo tumors faithfully recapitulated the structural and molecular features of parental tumors. Histological analysis revealed the rapid recruitment and spatial organization of heterogeneous host cancer-associated fibroblast (CAF) populations, showcasing distinct myofibroblastic and inflammatory stromal states. Crucially, the model preserved intrinsic tumor heterogeneity and permitted functional interrogation of subtype-specific extracellular matrix remodeling and metastatic dissemination. Together, our findings demonstrate that the CAM provides a highly permissive niche for tumor-stroma coevolution. As a rapid, scalable, and biologically relevant platform, this in ovo model offers a powerful approach for studying stromal composition, metastatic progression, and patient-specific tumor biology in pancreatic cancer.

cancer biology↗

bioRxiv2024

Establishing the foundations for a data-centric AI approach for virtual drug screening through a systematic assessment of the properties of chemical data

Researchers have adopted model-centric artificial intelligence (AI) approaches in cheminformatics by using newer, more sophisticated AI methods to take advantage of growing chemical libraries. It has been shown that complex deep learning methods outperform conventional machine learning (ML) methods in QSAR and ligand-based virtual screening1-3 but such approaches generally lack explanability. Hence, instead of developing more sophisticated AI methods (i.e., pursuing a model-centric approach), we wanted to explore the potential of a data-centric AI paradigm for virtual screening. A data-centric AI is an intelligent system that would automatically identify the right type of data to collect, clean and curate for later use by a predictive AI and this is required given the large volumes of chemical data that exist in chemical databases - PubChem alone has over 100 million unique compounds. However, a systematic assessment of the attributes and properties of suitable data is needed. We show here that it is not the result of deficiencies in current AI algorithms but rather, poor understanding and erroneous use of chemical data that ultimately leads to poor predictive performance. Using a new benchmark dataset of BRAF ligands that we developed, we show that our best performing predictive model can achieve an unprecedented accuracy of 99% with a conventional ML algorithm (SVM) using a merged molecular representation (Extended + ECFP6 fingerprints), far surpassing past performances of virtual screening platforms using sophisticated deep learning methods. Thus, we demonstrate that it is not necessary to resort to the use of sophisticated deep learning algorithms for virtual screening because conventional ML can perform exceptionally well if given the right data and representation. We also show that the common use of decoys for training leads to high false positive rates and its use for testing will result in an over-optimistic estimation of a models predictive performance. Another common practice in virtual screening is defining compounds that are above a certain pharmacological threshold as inactives. Here, we show that the use of these so-called inactive compounds lowers a models sensitivity/recall. Considering that some target proteins have a limited number of known ligands, we wanted to also observe how the size and composition of the training data impact predictive performance. We found that an imbalance training dataset where inactives outnumber actives led to a decrease in recall but an increase in precision, regardless of the model or molecular representation used; and overall, we observed a decrease in the models accuracy. We highlight in this study some of the considerations that one needs to take into account in future development of data-centric AI for CADD.

cancer biology↗

bioRxiv2017

Development of a Novel CD4+ TCR Transgenic Line that Reveals a Dominant Role for CD8+ DC and CD40-Signaling in the Generation of Helper and CTL Responses to Blood Stage Malaria

We describe an MHC II (IAb)-restricted T cell receptor (TCR) transgenic mouse line that produces CD4+ T cells specific for Plasmodium species. This line, termed PbT-II, was derived from a CD4+ T cell hybridoma generated to blood-stage Plasmodium berghei ANKA (PbA). PbT-II cells responded to all Plasmodium species and stages tested so far, including rodent (PbA, P. berghei NK65, P. chabaudi AS and P. yoelii 17XNL) and human (P. falciparum) blood-stage parasites as well as irradiated PbA sporozoites. PbT-II cells can provide help for generation of antibody to P. chabaudi infection and can control this otherwise lethal infection in CD40L-deficient mice. PbT-II cells can also provide help for development of CD8+ T cell-mediated experimental cerebral malaria (ECM) during PbA infection. Using PbT-II CD4+ T cells and the previously described PbT-I CD8+ T cells, we determined the dendritic cell (DC) subsets responsible for immunity to PbA blood-stage infection. CD8+ DC (a subset of XCR1+ DC) were the major antigen presenting cell (APC) responsible for activation of both T cell subsets, though other DC also contributed to CD4+ T cell responses. Depletion of CD8+ DC at the beginning of infection prevented ECM development and impaired both Th1 and Tfh responses; in contrast, late depletion did not affect ECM. This study describes a novel and versatile tool for examining CD4+ T cell immunity during malaria and provides evidence that CD4+ T cell help, acting via CD40L signalling, can promote immunity or pathology to blood stage malaria largely through antigen presentation by CD8+ DC.

immunology↗